Based on deeply analysis for optimization process of basic fruit fly optimization algorithm (FOA), a new improved FOA (IFOA) method is proposed, which modifies random search direction, increases the adjustment coefficient of search radius, and establishes a multi-sub-population solution mechanism. The proposed method can process the nonlinear objective function that has non-zero and non-negative extreme points. In the paper, IFOA method is applied to ill-conditioned problem solution in the field of surveying data processing. Application of the proposed method on two practical examples show that solution accuracy of IFOA is superior to that of three well-known intelligent optimization algorithms and two existing improved FOA methods, and it is also better than truncated singular value decomposition method and ridge estimation method. In addition, compared with intelligent search method represented by particle swarm optimization algorithm, The IFOA method has the advantages of less parameter settings, simple optimization process and easy program implementation. So, IFOA method is feasible, effective and practical in solving ill-conditioned problems.
Bridges are critical to economic and social development of a country. In order to ensure the safe operation of bridges, it is of great significance to accurately predict displacement of monitoring points from bridge Structural Health System (SHM). In the paper, a CEEMDAN-KELM model is proposed to improve the accuracy of displacement prediction of bridge. Firstly, the original displacement monitoring time series of bridge is accurately and effectively decomposed into multiple components called intrinsic mode functions (IMFs) and one residual component using a method named complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). Then, these components are forecasted by establishing appropriate kernel extreme learning machine (KELM) prediction models, respectively. At last, the prediction results of all components including residual component are summed as the final prediction results. A case study using global navigation satellite system (GNSS) monitoring data is used to illustrate the feasibility and validity of the proposed model. Practical results show that prediction accuracy using CEEMDAN-KELM model is superior to BP neural network model, EMD-ELM model and EMD-KELM model in terms of the same monitoring data.
Due to the increasing penetration of wind power into the distribution network, preserving system security and reliability becomes a significant challenge for the system operator. In the smart grid environment, the demand side is required to take more responsibilities to accommodate the uncertainty of wind power generations, known as demand response (DR). To enable this feature in the utility grid, system-wide costs, which include metering, communication and load control system upgrade cost and incentive cost for customers, should be considered in assessing cost-effectiveness. This paper proposes a novel optimization model for demand response facility (DRF) investment to determine the DR sizing and siting. Robust optimization is adopted to maintain overall economic benefit and distribution network operation security. The problem is formulated as a bi-level mixed-integer program. A column-and-constraint generation algorithm (C&CG) combined with outer-approximation (OA) linearization method is employed to solve this problem. Numerical tests on a modified IEEE 33-bus distribution network illustrate the effectiveness and validation of the proposed model.